feat: 模型推理异步加载与对话链路修复，同步基线

模型推理全异步化改造：
- 计算节点 InferenceSession 改为后台线程异步加载模型，load 立即返回，
  加载期间事件循环保持响应（/inference/status 与 /health 不阻塞）
- 后端模型加载改为异步派发 + 轮询对账器（reconcile_inference_loads），
  任务状态由 starting 自动推进到 ready/error，解决多节点启动超时
  （timeout of 120000ms exceeded）
- 推理删除/卸载改为任务感知 + 短超时，删除先删记录再 best-effort 卸载，
  不再被不可达节点阻塞；同节点新模型替换旧任务标记失效
- 流式对话透传 task_id/node_id 路由到真正加载模型的算力节点，
  useStreamChat 解析 SSE 错误帧以干净文案展示
- 对话历史按任务 id 本地持久化，退出重进可恢复；移除页脚提示文本
- 新增后端推理异步加载与计算节点异步状态机单元测试

Co-Authored-By: Claude <noreply@anthropic.com>

frontend/src/views/system/training-log/trainingLogModel.ts

@@ -1,7 +1,9 @@
 import type { EChartsOption } from 'echarts'
 import type { FineTuneTask, TrainingLogFile } from '@/types'
+import type { FineTuneMetricPoint } from '@/api/modules/fineTune'
 
 export interface TrainingMetricData {
+  steps: number[]
   loss: number[]
   gradNorm: number[]
   lr: number[]
@@ -26,7 +28,7 @@ function escapeRegExp(value: string) {
 }
 
 function extractNumber(source: string, key: string) {
-  const match = source.match(new RegExp(`['"]?${escapeRegExp(key)}['"]?\\s*:\\s*(${NUMBER_SOURCE})`, 'i'))
+  const match = source.match(new RegExp(`['"]?${escapeRegExp(key)}['"]?\\s*(?:=|:)\\s*(${NUMBER_SOURCE})`, 'i'))
   return match ? Number(match[1]) : undefined
 }
 
@@ -57,24 +59,44 @@ export function resolveTrainingLogFile(
 
 /** 解析日志中的逐步指标。字段顺序和常见数值格式均不受限制。 */
 export function parseTrainingMetrics(text: string): TrainingMetricData {
-  const metrics: TrainingMetricData = { loss: [], gradNorm: [], lr: [], epoch: [] }
-  const blocks = text.match(/\{[^{}\r\n]*\}/g) || []
+  const metrics: TrainingMetricData = { steps: [], loss: [], gradNorm: [], lr: [], epoch: [] }
+  const candidates = text
+    .split(/\r?\n/)
+    .flatMap((line) => {
+      const blocks = line.match(/\{[^{}\r\n]*\}/g)
+      return blocks?.length ? blocks.map((block) => `${line} ${block}`) : [line]
+    })
 
-  for (const block of blocks) {
-    const loss = extractNumber(block, 'loss')
-    const gradNorm = extractNumber(block, 'grad_norm')
-    const learningRate = extractNumber(block, 'learning_rate')
-    const epoch = extractNumber(block, 'epoch')
-    if (loss == null || gradNorm == null || learningRate == null) continue
-    metrics.loss.push(loss)
-    metrics.gradNorm.push(gradNorm)
-    metrics.lr.push(learningRate)
-    if (epoch != null) metrics.epoch.push(epoch)
+  for (const [index, line] of candidates.entries()) {
+    const loss = extractNumber(line, 'loss')
+    const gradNorm = extractNumber(line, 'grad_norm')
+    const learningRate = extractNumber(line, 'learning_rate')
+    const epoch = extractNumber(line, 'epoch')
+    if (loss == null && gradNorm == null && learningRate == null) continue
+    metrics.steps.push(extractNumber(line, 'step') ?? metrics.steps.length + index + 1)
+    metrics.loss.push(loss ?? Number.NaN)
+    metrics.gradNorm.push(gradNorm ?? Number.NaN)
+    metrics.lr.push(learningRate ?? Number.NaN)
+    metrics.epoch.push(epoch ?? Number.NaN)
   }
 
   return metrics
 }
 
+export function metricsFromApi(points: FineTuneMetricPoint[]): TrainingMetricData {
+  const metrics: TrainingMetricData = { steps: [], loss: [], gradNorm: [], lr: [], epoch: [] }
+  for (const [index, point] of points.entries()) {
+    const hasMetric = point.loss != null || point.grad_norm != null || point.learning_rate != null
+    if (!hasMetric) continue
+    metrics.steps.push(Number(point.step || index + 1))
+    metrics.loss.push(point.loss == null ? Number.NaN : Number(point.loss))
+    metrics.gradNorm.push(point.grad_norm == null ? Number.NaN : Number(point.grad_norm))
+    metrics.lr.push(point.learning_rate == null ? Number.NaN : Number(point.learning_rate))
+    metrics.epoch.push(point.epoch == null ? Number.NaN : Number(point.epoch))
+  }
+  return metrics
+}
+
 /** 每次都返回新对象，日志截断或切换时不会残留上一轮汇总。 */
 export function parseTrainingSummary(text: string): TrainingSummary {
   const emptySummary: TrainingSummary = { epoch: '', trainLoss: '', runtime: '' }
@@ -102,11 +124,23 @@ export function parseTrainingLog(text: string): ParsedTrainingLog {
 export function buildMetricChartOption(
   label: string,
   data: number[],
+  steps: number[],
   color: string,
   logScale = false,
 ): EChartsOption {
+  const visibleData = data.map((value) => (Number.isFinite(value) ? value : null))
   return {
     grid: { top: 24, right: 20, bottom: 56, left: 56 },
+    graphic: visibleData.some((value) => value != null)
+      ? []
+      : [
+          {
+            type: 'text',
+            left: 'center',
+            top: 'middle',
+            style: { text: '暂无训练指标数据', fill: '#94a3b8', fontSize: 13 },
+          },
+        ],
     tooltip: {
       trigger: 'axis',
       axisPointer: { type: 'cross' },
@@ -116,6 +150,7 @@ export function buildMetricChartOption(
     },
     xAxis: {
       type: 'category',
+      data: steps.map((step, index) => (Number.isFinite(step) ? String(step) : String(index + 1))),
       boundaryGap: false,
       name: 'Step',
       nameTextStyle: { color: '#94a3b8', fontSize: 11 },
@@ -142,7 +177,7 @@ export function buildMetricChartOption(
       {
         name: label,
         type: 'line',
-        data,
+        data: visibleData,
         smooth: true,
         symbol: 'none',
         lineStyle: { width: 2, color },